1. Technical Field
The present subject matter presents a novel system for autonomous predictive analysis using data from multiple fused sensor inputs stored in non-transitory computer memory.
2. Related Art
Sensors and the means of information systems to communicate are well-known. Known sensors and communication systems require ever-more bandwidth to provide end-users with a real-time scenario understanding or “situational awareness.” Better situational awareness is created by an amalgamation of sensor inputs. If current communication bandwidth limitations can be mitigated, end-users can attain synchronized, high-speed analysis and decision-making.
Ever-increasing bandwidth does not efficiently resolve every problem related to large quantities of data from diverse sources of data, each of which generate a different type of data. For example, increased bandwidth cannot autonomously fuse diverse information inputs, use the diverse information for near real-time decision-making, and then acquire an ever-greater expertise to do so from observing an end-user's behavior. This means for acquiring, digesting, delivering, and updating, the information intended for end-users is referred to in the art as “machine-learning.”
Forms of machine learning are known in the art. Heuristic solutions have been used for mission planning. These approaches and algorithms are referred to in the art as “agent-based,” or “rule-based.”
A limitation of rule-based systems known in the art is that they cannot simulate a human decision maker's thought process. Human analysts must ultimately interpret and manipulate data to move the data up the authoritative hierarchy. Information must be synthesized in a decision maker's head since sensors can only gather data without any predictive capabilities. Agent-based systems can only make decisions based upon immediately gathered data in relation to set rules.
“Fuzzy” inference is a term known in the art for heuristically processing data when an exact case match is not possible. Heuristic systems are known in the art that perform such processes, however, these systems are not yet capable of making complex associations between gathered data and inferential results.
There is an unmet need for computer systems, including autonomous vehicle systems, capable of making associations necessary to filter extraneous data input and minimize demands on human resources and communication.
There is a further unmet need that intelligence and data gathering systems be able to learn from patterns of gathered data and to more closely approximate human decisions, reducing the need for human interaction.